For many years the neuromodulator adenosine has been recognized as an endogenous anticonvulsant molecule and termed a “retaliatory metabolite.” As the core molecule of ATP, adenosine forms a unique link between cell energy and neuronal excitability. In parallel, a ketogenic (high-fat, low-carbohydrate) diet is a metabolic therapy that influences neuronal activity significantly, and ketogenic diets have been used successfully to treat medically-refractory epilepsy, particularly in children, for decades. To date the key neural mechanisms underlying the success of dietary therapy are unclear, hindering development of analogous pharmacological solutions. Similarly, adenosine receptor–based therapies for epilepsy and myriad other disorders remain elusive. In this review we explore the physiological regulation of adenosine as an anticonvulsant strategy and suggest a critical role for adenosine in the success of ketogenic diet therapy for epilepsy. While the current focus is on the regulation of adenosine, ketogenic metabolism and epilepsy, the therapeutic implications extend to acute and chronic neurological disorders as diverse as brain injury, inflammatory and neuropathic pain, autism and hyperdopaminergic disorders. Emerging evidence for broad clinical relevance of the metabolic regulation of adenosine will be discussed.

www.ClinicalTrials.gov, study number NCT02730637.

Background In acute decompensated heart failure (ADHF) the risk of acute kidney injury (AKI) is high. Early detection of patients at risk for AKI is important. We tested urinary [TIMP‐2] × [IGFBP7], a new US Food and Drug Administration–cleared test to assess AKI risk, in a cohort of hospitalized ADHF patients. Hypothesis In patients with ADHF, urinary [TIMP‐2] × [IGFBP7] is associated with moderate to severe AKI and related to increased mortality. Methods We enrolled 400 patients in the emergency department at Robert‐Bosch Hospital, Stuttgart, Germany. We examined the predictive ability of urinary [TIMP‐2] × [IGFBP7] (units: [ng/mL]2/1000) for development of AKI stage 2 or 3 within 24 hours of sample collection in patients with ADHF. Operating characteristics were determined for the validated cutoffs of 0.3 and 2.0. Results Forty patients had ADHF upon presentation and sufficient data for AKI staging. 27.5% developed AKI stage 2–3 within 7 days. Urinary [TIMP‐2] × [IGFBP7] discriminated for AKI stage 2–3 over the first day with an area under the ROC curve of 0.84 (95% confidence interval: 0.72‐0.93) and over 7 days with an AUC of 0.77 (95% confidence interval: 0.65‐0.88). For the first day, sensitivity was 86% at the 0.3 cutoff and specificity was 95% at the 2.0 cutoff for prediction of AKI stage 2–3. There was a trend (P = 0.08) for higher mortality in patients with urinary [TIMP‐2] × [IGFBP7] >2.0 and AKI 2–3. Conclusions Urinary [TIMP‐2] × [IGFBP7] is a promising marker for AKI risk assessment in patients with ADHF.

Background and objectives Emergency departments (EDs) have a growing role in hospital admissions, but few studies address AKI biomarkers in the ED.Design, setting, participants, & measurements Patients admitted to the internal medicine service were enrolled during initial workup in the ED at Robert-Bosch-Hospital, Stuttgart, Germany. Daily serum creatinine (sCr) and urine output (UO) were recorded for AKI classification by Kidney Disease Improving Global Outcomes (KDIGO) criteria. Cystatin C, kidney injury molecule-1, liver-type fatty acid-binding protein, and neutrophil gelatinaseassociated lipocalin were measured in blood and urine, and IL-18, insulin-like growth factor-binding protein 7 (IGFBP7), tissue inhibitor of metalloproteinases-2 (TIMP-2) and [TIMP-2]·[IGFBP7] were measured in urine collected at enrollment, after 6 hours, and the following morning. Association between these biomarkers and the end point of moderate-severe AKI (KDIGO stage 2-3) occurring within 12 hours of each sample collection was examined using generalized estimating equation logistic regression. Performance for prediction of the AKI end point using two previously validated [TIMP-2]-[IGFBP7] cutoffs was also tested.Results Of 400 enrolled patients, 298 had sufficient sCr and UO data for classification by KDIGO AKI criteria: AKI stage 2 developed in 37 patients and AKI stage 3 in nine patients. All urinary biomarkers, sCr, and plasma cystatin C had statistically significant (P,0.05) odds ratios (ORs) for the AKI end point. In a multivariable model of the urine biomarkers and sCr, only

Background/Aims: There is a growing role for emergency departments (ED) in assessing acute kidney injury (AKI) for hospital admissions but there are few studies addressing acute kidney injury biomarkers and confounding factors in the ED. Cystatin C (CysC), a newer renal biomarker, is influenced by thyroid function, inflammation and obesity. This study aims to be the first study to address the impact of these parameters in the ED. Methods: Admitted patients (n=397) were enrolled in the ED at Robert-Bosch-Hospital, Stuttgart, Germany. Daily serum creatinine (sCr) was recorded for AKI classification by Kidney Diseases Improves Global Outcome (KDIGO) criteria. CysC, thyroid stimulating hormone (TSH), thyroxine (T4), C-reactive protein (CRP) and body mass index (BMI) were registered at enrollment in the ED. Serum samples were collected at enrollment, after 6 hours and in the following mornings (day 1 to day 3). The correlation of CysC and sCr was studied on a two variable logistic regression model. A linear predictor was computed to predict minimal AKI stage and area under the curve (AUC) was calculated. Results: Of 397 patients enrolled for classification by KDIGO AKI criteria, n=152 (38%) developed AKI, n=69 (17.4%) reached AKI stage I, n=70 (17.6%) AKI stage II, and n=13 (3%) AKI stage III. Although a correlation between CRP and CysC levels was shown (rho=0.376), this didn't affect the predictive ability for AKI according to our data. We compared receiver operating characteristic (ROC) curves (DeLong test) of CysC to ROC curves of CysC with the additional variables TSH, BMI, and CRP respectively. Our data shows that addition of CRP, TSH, or BMI does not improve prediction of AKI stage beyond prediction based solely on CysC levels. Conclusions: CysC is known to be influenced by thyroid function, inflammation and obesity, but in our large ED population there was no significant impact of these factors on the diagnostic accuracy of CysC to detect AKI in ED patients.